In this course, you will learn how to translate various concepts in Snowflake to the analogous concepts in BigQuery. You will learn how the high-level architectures of Snowflake and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Snowflake to data types in BigQuery, understand schema mapping from Snowflake to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Snowflake and BigQuery.
Course Objectives
- Comparing architecture and provisioning of resources in Snowflake and BigQuery
- Configuring datasets and tables in BigQuery
- Mapping data types in Snowflake to data types in BigQuery
- Mapping and optimizing schemas from Snowflake to BigQuery
- SQL translation from Snowflake to BigQuery
Audience
Current users of Snowflake (Data Engineers, Data Analysts, Data Scientists, Application Developers) migrating to BigQuery.
Prerequisites
Completion of a course covering an introduction to BigQuery (e.g., ”From Data to Insights with Google Cloud”) or equivalent experience using BigQuery.
Course Outline
Module 1: Understanding BigQuery Architecture
- Quick reminder of Snowflake architecture
- Overview of BigQuery architecture
- Separation of compute and storage in BigQuery
- BigQuery Slots
- Workload management in BigQuery
Module 2: Creating Datasets and Tables in BigQuery
- Resource Hierarchy in Snowflake
- Resource Hierarchy in BigQuery
- Creating resources in BigQuery
- Sharing resources in BigQuery
- Lab: Provisioning and Managing Resources in BigQuery
Module 3: Mapping Data Types from Snowflake to BigQuery
- How data types map from Snowflake to BigQuery
- Understand data types unique to BigQuery
Module 4: Schema Mapping and Optimization
- Schema definitions in BigQuery
- Partitioning in BigQuery
- Clustering in BigQuery
- Lab: Schema Migration to BigQuery
Module 5: SQL Translation from Snowflake to BigQuery
- SELECT statements
- DML statements
- DDL statements
- UDFs and Procedures
- Lab: Writing SQL for BigQuery